A central goal of neuroscience is to understand how learning algorithms are implemented by neurons and muscles. However, despite decades of psychophysical studies in humans, our understanding of how motor learning is implemented physiologically is rudimentary. A critical gap therefore exists between psychophysical models of learning and the physiological changes in the motor program that reshape behavior. Songbirds provide a physiologically accessible model system in which to investigate behavioral plasticity. However, song learning has previously been studied on timescales too long to allow single-neuron recordings, making it impossible to identify the changes in neural activity that underlie learning. Furthermore, the functions of the song muscles themselves are poorly understood, limiting our understanding of how vocal muscles and the neurons that activate them control behaviorally important acoustic parameters. The proposed experiments overcome these obstacles by combining behavioral and computational approaches drawn from human motor psychophysics with the neurophysiological accessibility of the songbird system, linking learning algorithms to neurons and muscles. Our long-term goal is to understand how the brain controls and modifies vocal output as an animal acquires vocal behaviors and maintains vocal performance throughout its lifetime. The objective of the proposed experiments is to reveal how a single acoustic parameter - fundamental frequency (pitch) - is modified during short-term vocal error correction. Our central hypothesis is that pitch learning depends strongly on the statistics of prior sensorimotor experience, that vocal muscles exert bidirectional influence on pitch across different vocal gestures (song syllables), and that pitch learning is implemented by altering the spike content of bursts fired by neurons in a forebrain premotor nucleus. Drawing on significant quantities of preliminary data, three specific aims will test this hypothesis. The first aim will challenge current theories of vocal learning by using manipulations of auditory feedback to drive adaptive pitch changes in singing birds. The second specific aim will quantify the functions of individual vocal muscles and reveal how muscle activity changes during learning by combining precisely-timed muscle stimulation, behavioral manipulations, and EMG recordings. The third aim will (for the first time) define the changes neural activity that underlie vocal learning by recording from single neurons during a rapid vocal learning paradigm, identifying a locus of vocal motor plasticity and establishing the songbird as one of the only available systems for studying changes in neural activity during online learning. This approach is innovative because it allows us to detect changes in motor command signals online during learning, providing a critical link between behavioral and physiological approaches to motor learning. These studies are significant because a better understanding of the mechanisms of sensorimotor learning could aid in the design of rehabilitative strategies that exploit the plasticity of complex behavio.